In this paper, we collect an ocular B-ultrasound image dataset and propose a Collaborative Monitoring Deep Learning (CMDL) method to detect cataract. In the ocular B-ultrasound images, there are often strong echoes near the posterior capsule and lens, and the fine-grained ocular B-ultrasound images are often accompanied by the characteristics of weak penetrating power, low contrast, narrow imaging range, and high noise. Thus, in the proposed CMDL method, we introduce an object detection network based on YOLO-v3 to detect the focus areas we need, so as to reduce the interference of noise and improve the cataract detection accuracy of our method. Considering that the B-ultrasound image dataset we collected is small-scale, we also design three feature extraction modules to avoid over-fitting of the deep neural networks. Among them, there are a depth features extraction module based on DenseNet-161, a shape features extractor based on Fourier descriptor, and a texture features extraction module based on gray-level co-occurrence matrix. Moreover, we also introduce the collaborative learning module to improve the generalization of the proposed model. Specifically, we first fuse the depth, shape, and texture features of the eyeball and lens, respectively. Then, the fused features of the eyeball and lens are concatenated as the input of collaborative network. Finally, the introduced classification loss with the aid of collaborative loss, which distinguishes whether the eyeball and lens belong to the same category, improves the classification accuracy in cataract detection. Experimental results on our collected dataset demonstrate the effectiveness of the proposed CMDL method.

在本文中，我们收集了一个眼部 B 超声图像数据集，并提出了一种协作监测深度学习 (CMDL) 方法来检测白内障。眼部B超图像中，后囊和晶状体附近常有强回声，细粒度的眼部B超图像常伴有穿透力弱、对比度低、成像范围窄、高噪音。因此，在提出的CMDL方法中，我们引入了一个基于YOLO-v3的物体检测网络来检测我们需要的焦点区域，以减少噪声的干扰，提高我们方法的白内障检测精度。考虑到我们收集的 B 超图像数据集是小规模的，我们还设计了三个特征提取模块来避免深度神经网络的过度拟合。他们之中，有一个基于DenseNet-161的深度特征提取模块，一个基于傅立叶描述符的形状特征提取器，一个基于灰度共生矩阵的纹理特征提取模块。此外，我们还引入了协作学习模块来提高所提出模型的泛化能力。具体来说，我们首先分别融合眼球和晶状体的深度、形状和纹理特征。然后，将眼球和晶状体的融合特征串联起来作为协同网络的输入。最后，在协同损失的帮助下引入分类损失，区分眼球和晶状体是否属于同一类别，提高了白内障检测的分类精度。在我们收集的数据集上的实验结果证明了所提出的 CMDL 方法的有效性。